Substructure Mining of GPCR Ligands Reveals Activity-Class Specific Functional Groups in an Unbiased Manner

In this study, we conducted frequent substructure mining to identify structural features that discriminate between ligands that do bind to G protein-coupled receptors (GPCRs) and those that do not. In most cases, particular chemical representations resulted in the most significant substructures. Substructures found to be characteristic for the background control set reflected reactions that may have been used to construct this library, e.g., for the ChemBridge DIVERSet library employed these are ester and carboxamide moieties. Alkane amine substructures were identified as most important for GPCR ligands, e.g. the butylamine substructure, often linked to an aromatic system. Hierarchical analysis of targeted GPCRs revealed well-known motives and new substructural features. One example is the imidazole-like substructure common for the histamine binding receptor ligands. Another example is the planar ring system consisting of a fused five- and six-membered ring (indole-like substucture) common for the serotonin receptor ligands.

[1]  Ola Engkvist,et al.  Prediction of CNS Activity of Compound Libraries Using Substructure Analysis , 2003, J. Chem. Inf. Comput. Sci..

[2]  T. Klabunde,et al.  GPCR Antitarget Modeling: Pharmacophore Models for Biogenic Amine Binding GPCRs to Avoid GPCR‐Mediated Side Effects , 2005, Chembiochem : a European journal of chemical biology.

[3]  R. Glen,et al.  Molecular similarity: a key technique in molecular informatics. , 2004, Organic & biomolecular chemistry.

[4]  Konstantin V. Balakin,et al.  Property-Based Design of GPCR-Targeted Library , 2002, J. Chem. Inf. Comput. Sci..

[5]  Andreas Bender,et al.  Molecular Similarity Searching Using Atom Environments, Information-Based Feature Selection, and a Naïve Bayesian Classifier , 2004, J. Chem. Inf. Model..

[6]  H. M. Vinkers,et al.  SYNOPSIS: SYNthesize and OPtimize System in Silico. , 2003, Journal of medicinal chemistry.

[7]  Ray M. Marín,et al.  Graph Theoretical Similarity Approach To Compare Molecular Electrostatic Potentials , 2008, J. Chem. Inf. Model..

[8]  Jürgen Bajorath,et al.  Chemical Database Mining through Entropy-Based Molecular Similarity Assessment of Randomly Generated Structural Fragment Populations , 2007, J. Chem. Inf. Model..

[9]  Gert Vriend,et al.  GPCRDB information system for G protein-coupled receptors , 2003, Nucleic Acids Res..

[10]  G. Bemis,et al.  The properties of known drugs. 1. Molecular frameworks. , 1996, Journal of medicinal chemistry.

[11]  Ramaswamy Nilakantan,et al.  A Family of Ring System-Based Structural Fragments for Use in Structure-Activity Studies: Database Mining and Recursive Partitioning , 2006, J. Chem. Inf. Model..

[12]  Jürgen Bajorath,et al.  Assessment of Molecular Similarity from the Analysis of Randomly Generated Structural Fragment Populations. , 2006 .

[13]  Satoshi Niijima,et al.  GLIDA: GPCR—ligand database for chemical genomics drug discovery—database and tools update , 2007, Nucleic Acids Res..

[14]  A. H. Lipkus,et al.  Structural Diversity of Organic Chemistry. a Scaffold Analysis of the Cas Registry , 2022 .

[15]  James G. Nourse,et al.  Reoptimization of MDL Keys for Use in Drug Discovery , 2002, J. Chem. Inf. Comput. Sci..

[16]  J. Bajorath,et al.  Distribution of Molecular Scaffolds and R-Groups Isolated from Large Compound Databases , 1999 .

[17]  M D Barratt,et al.  The computational prediction of toxicity. , 2001, Current opinion in chemical biology.

[18]  Thorsten Meinl,et al.  A Quantitative Comparison of the Subgraph Miners MoFa, gSpan, FFSM, and Gaston , 2005, PKDD.

[19]  Christian Borgelt,et al.  Mining molecular fragments: finding relevant substructures of molecules , 2002, 2002 IEEE International Conference on Data Mining, 2002. Proceedings..

[20]  G. Bemis,et al.  Properties of known drugs. 2. Side chains. , 1999, Journal of medicinal chemistry.

[21]  H. Meltzer,et al.  Treatment-resistant schizophrenia--the role of clozapine. , 1997, Current medical research and opinion.

[22]  Thomas Bäck,et al.  Mining a Chemical Database for Fragment Co-occurrence: Discovery of "Chemical Clichés" , 2006, J. Chem. Inf. Model..

[23]  A. Bender,et al.  Circular fingerprints: flexible molecular descriptors with applications from physical chemistry to ADME. , 2006, IDrugs : the investigational drugs journal.

[24]  Jun Xu A new approach to finding natural chemical structure classes. , 2002, Journal of medicinal chemistry.

[25]  Thomas Bäck,et al.  Substructure Mining Using Elaborate Chemical Representation , 2006, J. Chem. Inf. Model..

[26]  Miklos Feher,et al.  Property Distributions: Differences Between Drugs, Natural Products, and Molecules from Combinatorial Chemistry. , 2003 .

[27]  Peter Willett,et al.  Maximum common subgraph isomorphism algorithms for the matching of chemical structures , 2002, J. Comput. Aided Mol. Des..

[28]  C. Strader,et al.  Conserved aspartic acid residues 79 and 113 of the beta-adrenergic receptor have different roles in receptor function. , 1988, The Journal of biological chemistry.

[29]  Andreas Bender,et al.  Similarity Searching of Chemical Databases Using Atom Environment Descriptors (MOLPRINT 2D): Evaluation of Performance , 2004, J. Chem. Inf. Model..

[30]  Adriaan P. IJzerman,et al.  Computational Approaches to Fragment and Substructure Discovery and Evaluation , 2008 .

[31]  Dora M Schnur,et al.  Are target-family-privileged substructures truly privileged? , 2006, Journal of medicinal chemistry.

[32]  Gerhard Hessler,et al.  Drug Design Strategies for Targeting G‐Protein‐Coupled Receptors , 2002, Chembiochem : a European journal of chemical biology.

[33]  Bryan L Roth,et al.  Screening the receptorome to discover the molecular targets for plant-derived psychoactive compounds: a novel approach for CNS drug discovery. , 2004, Pharmacology & therapeutics.

[34]  M. D. Lindner Clinical attrition due to biased preclinical assessments of potential efficacy. , 2007, Pharmacology & therapeutics.

[35]  Edgar Jacoby,et al.  A Three Binding Site Hypothesis for the Interaction of Ligands with Monoamine G Protein‐coupled Receptors: Implications for Combinatorial Ligand Design , 1999 .

[36]  Matthew H. Todd Computer-Aided Organic Synthesis , 2005 .

[37]  Richard R. Neubig,et al.  International Union of Pharmacology. XLVI. G Protein-Coupled Receptor List , 2005, Pharmacological Reviews.

[38]  J. Kazius,et al.  Derivation and validation of toxicophores for mutagenicity prediction. , 2005, Journal of medicinal chemistry.

[39]  C. Ragan,et al.  Schizophrenia and L-745,870, a novel dopamine D4 receptor antagonist. , 1997, Trends in pharmacological sciences.

[40]  G. Bemis,et al.  A minimalist approach to fragment‐based ligand design using common rings and linkers: Application to kinase inhibitors , 2004, Proteins.

[41]  Robert P. Sheridan,et al.  A Method for Visualizing Recurrent Topological Substructures in Sets of Active Molecules , 2010 .

[42]  Robert P Bywater,et al.  Recognition of privileged structures by G-protein coupled receptors. , 2004, Journal of medicinal chemistry.

[43]  Joost N. Kok,et al.  A quickstart in frequent structure mining can make a difference , 2004, KDD.